Abstract
Simultaneous Localization and Mapping (SLAM) is a critical task for autonomous navigation. However, due to the computational complexity of SLAM algorithms, it is very difficult to achieve real-time implementation on low-power platforms.We propose an energy efficient architecture for real-time ORB (Oriented-FAST and Rotated- BRIEF) based visual SLAM system by accelerating the most time consuming stages of feature extraction and matching on FPGA platform.Moreover, the original ORB descriptor pattern is reformed as a rotational symmetric manner which is much more hardware friendly. Optimizations including rescheduling and parallelizing are further utilized to improve the throughput and reduce the memory footprint. Compared with Intel i7 and ARM Cortex-A9 CPUs on TUM dataset, our FPGA realization achieves up to 3X and 31X frame rate improvement, as well as up to 71X and 25X energy efficiency improvement, respectively.
Abstract (translated)
同时定位和映射（SLAM）是自主导航的关键任务。然而，由于SLAM算法的计算复杂，在低功耗平台上实现实时性非常困难，我们通过加速最耗时的特征提取和匹配阶段，提出了一种基于实时ORB（快速旋转）的视觉SLAM系统的节能架构。在现场可编程门阵列（FPGA）平台上，将原有的ORB描述符模式改造为旋转对称模式，使其硬件更加友好。进一步利用包括重新调度和并行化在内的优化来提高吞吐量和减少内存占用。与TUM数据集上的Intel i7和ARM Cortex-A9 CPU相比，我们的FPGA实现了高达3倍和31倍的帧速率改进，以及高达71倍和25倍的能效改进。
URL
https://arxiv.org/abs/1906.05096
